黄河流域植被覆盖度变化及驱动因素

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Abstract The pixel binary model was used to inverse the fractional vegetation cover (FVC) from 1999 to 2019 based on the Google Earth Engine platform, and the unary linear regression analysis and the coefficient of variance method were used to study the changes in the FVC trend characteristics and stability. The geographic detector was used to analyze the driving force of vegetation change. The results showed that: (1) The FVC of the Yellow River Basin was generally low in the northwest and high in the Southeast; the medium-high and high-covered areas accounted for 21.74% and 17.87% of the study area, respectively; the FVC of the Yellow River Basin had improved well in the past two decades. The improvement of vegetation in the middle reaches of the basin was the most obvious, and the improvement area accounted for 48.52% of the total area in our study area; the stability of FVC was mainly stable. (2) The three driving factors of precipitation, sunshine duration, and relative humidity had the strongest influence on FVC in the Yellow River Basin. (3) Each driving factor had an interaction effect on the FVC. The two-factor enhancement or nonlinear enhancement was the main factor. The two-factor interaction enhanced the impact of the single factor. This study also revealed the most suitable range of factors that promoted vegetation growth. These results help to better understand the impact of natural and social factors on vegetation cover changes and their driving mechanisms.

Key words： fractional vegetation cover spatiotemporal evolution driving forces geographic detector Yellow River Basin

Received: 12 April 2022

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X87

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	WANG Xiao-lei
	SHI Shou-hai
	CHEN jiang-zhao-xia

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WANG Xiao-lei,SHI Shou-hai,CHEN jiang-zhao-xia. Change and driving factors of vegetation coverage in the Yellow River Basin[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(11): 5358-5368.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I11/5358

[1]	Wang B, Jia K, Wei X, et al. Generating spatiotemporally consistent fractional vegetation cover at different scales using spatiotemporal fusion and multiresolution tree methods[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020,167:214-229.
[2]	Liu C, Li W, Zhu G, et al. Land use/land cover changes and their driving factors in the Northeastern Tibetan Plateau based on geographical detectors and google earth engine:a case study in Gannan prefecture[J]. Remote Sensing, 2020,12(19):3139.
[3]	Yan K, Gao S, Chi H, et al. Evaluation of the vegetation-index-based dimidiate pixel model for fractional vegetation cover estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021,60:1-14.
[4]	Jia K, Li Y, Liang S, et al. Combining estimation of green vegetation fraction in an arid region from Landsat 7ETM+ data[J]. Remote Sensing, 2017,9(11):1121.
[5]	Jia K, Liang S, Gu X, et al. Fractional vegetation cover estimation algorithm for Chinese GF-1wide field view data[J]. Remote Sensing of Environment, 2016,177:184-191.
[6]	Zhang W, Wang L, Xiang F, et al. Vegetation dynamics and the relations with climate change at multiple time scales in the Yangtze River and Yellow River Basin, China[J]. Ecological Indicators, 2020,110:105892.
[7]	张志强,刘欢,左其亭,等.2000~2019年黄河流域植被覆盖度时空变化[J]. 资源科学, 2021,43(4):849-858. Zhang Z Q, Liu H, Zuo Q T, et al. Spatiotemporal change of fractional vegetation cover in the Yellow River Basin during 2000~2019[J]. Resources Science, 2021,43(4):849-858.
[8]	Tian F, Liu L Z, Yang J H, et al. Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities[J]. Ecological Indicators, 2021,125:107479.
[9]	李春晖,杨志峰.黄河流域NDVI时空变化及其与降水/径流关系[J]. 地理研究, 2004,(6):753-759. Li C H,Yang Z F. Spatio-temporal changes of NDVI and their relations with precipitation and runoff in the Yellow River Basin[J]. Geograp hical Research, 2004,23(6):753-759.
[10]	李晶,闫星光,闫萧萧,等.基于GEE云平台的黄河流域植被覆盖度时空变化特征[J]. 煤炭学报, 2021,46(5):1439-1450. Li J, Yan X G, Yan X X, et al. Temporal and spatial variation characteristic of vegetation coverage in the Yellow River Basin based on GEE cloud platform[J]. Journal of China Coal Society, 2021,46(5):1439-1450.
[11]	贺振,贺俊平.近32年黄河流域植被覆盖时空演化遥感监测[J]. 农业机械学报, 2017,48(2):179-185. He Z, He J P.Remote sensing on spatio-temporal evolution of vegetation cover in the Yellow River Basin during 1982~2013[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(2):179-185.
[12]	袁丽华,蒋卫国,申文明,等.2000~2010年黄河流域植被覆盖的时空变化[J]. 生态学报, 2013,33(24):7798-7806. Yuan L H, Jiang W G, Shen W M, et al. The spatio-temporal variations of vegetation cover in the Yellow River Basin from 2000 to 2010[J]. Acta Ecologica Sinica, 2013,33(24):7798-7806.
[13]	Peng W, Kuang T, Tao S. Quantifying influences of natural factors on vegetation NDVI changes based on geographical detector in Sichuan, western China[J]. Journal of Cleaner Production, 2019,233:353-367.
[14]	Jiang M, He Y, Song C, et al. Disaggregating climatic and anthropogenic influences on vegetation changes in Beijing-Tianjin-Hebei region of China[J]. Science of The Total Environment, 2021, 786:147574.
[15]	Nie T, Dong G, Jiang X, et al. Spatio-temporal changes and driving forces of vegetation coverage on the Loess Plateau of Northern Shaanxi[J]. Remote Sensing, 2021,13(4):613.
[16]	Liu X, Ou J, Chen Y, et al. Scenario simulation of urban energy-related CO2 emissions by coupling the socioeconomic factors and spatial structures[J]. Applied Energy, 2019,238:1163-1178.
[17]	Jiao W, Wang L, Smith W K, et al. Observed increasing water constraint on vegetation growth over the last three decades[J]. Nature Communications, 2021,12(1):3777.
[18]	Zuo D, Han Y, Xu Z, et al. Time-lag effects of climatic change and drought on vegetation dynamics in an alpine river basin of the Tibet Plateau, China[J]. Journal of Hydrology, 2021,600:126532.
[19]	秦格霞,芦倩,孟治元,等.1982~2015年中国北方草地NDVI时空动态及其对气候变化的响应[J]. 水土保持研究, 2021,28(1):101-108,17. Qin G X, Lu Q, Meng Z Y, et al. Spatial-temporal dynamics of grassland NDVI and its response to climate change in Northern China from 1982 to 2015. Research of Soil and Water Conservation, 2021, 28(1):101-108,117.
[20]	Gorelick N, Hancher M, Dixon M, et al. Google earth engine:planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017,202:18-27.
[21]	李苗苗,吴炳方,颜长珍,等.密云水库上游植被覆盖度的遥感估算[J]. 资源科学, 2004,(4):153-159. Li M M, Wu B F, Yan Z Z, et al. Estimation of vegetation fraction in the upper basin of Miyun reservoir by remote sensing[J]. Resources Science, 2004,(4):153-159.
[22]	孟琪,武志涛,杜自强,等.基于地理探测器的区域植被覆盖度的定量影响——以京津风沙源区为例[J]. 中国环境科学, 2021,41(2):826-836. Meng Q, Wu Z T, Du Z Q, et al. Quantitative influence of regional fractional vegetation cover based on geodetector model-take the Beijing-Tianjin sand source region as an example.[J]. China Environmental Science, 2021,41(2):826-836.
[23]	何宝忠,丁建丽,张喆,等.新疆植被覆盖度趋势演变实验性分析[J]. 地理学报, 2016,71(11):1948-1966. He B Z, Ding J L, Zhang Z, et al. Experimental analysis of spatial and temporal dynamics of fractional vegetation cover in Xinjiang[J]. Acta Geographica Sinica, 2016,71(11):1948-1966.
[24]	Wang J F, Zhang T L, Fu B J. A measure of spatial stratified heterogeneity[J]. Ecological Indicators, 2016,67:250-256.
[25]	王劲峰,徐成东.地理探测器:原理与展望[J]. 地理学报, 2017,2(1):116-134. Wang J F, Xu C D. Geodetector:principle and prospective[J]. Acta Geographica Sinica, 2017,72(1):116-134.
[26]	彭文甫,张冬梅,罗艳玫,等.自然因子对四川植被NDVI变化的地理探测[J]. 地理学报, 2019,74(9):1758-1776. Peng W F, Zhang D M, Luo Y M, et al. Influence of natural factors on vegetation NDVI using geographical detection in Sichuan province[J]. Acta Geographica Sinica, 2019,74(9):1758-1776.
[27]	赵晓冏,苏军德,王建,等.甘肃省生态服务供需关系及影响因子研究[J]. 中国环境科学, 2021,41(10):4926-4941. Zhao X J, Su J D, Wang J, et al. A study on the relationship between supply-demand relationship of ecosystem services and impact factors in Gansu province based on geographical detector[J]. China Environmental Science, 2021,41(10):4926-4941.
[28]	Liang P, Yang X. Landscape spatial patterns in the Maowusu (Mu Us) sandy land, northern China and their impact factors[J]. Catena, 2016, 145:321-33.
[29]	潘洪义,黄佩,徐婕.基于地理探测器的岷江中下游地区植被NPP时空格局演变及其驱动力研究[J]. 生态学报, 2019,39(20):7621-7631. Pan H Y, Huang P, Xu J. The spatial and temporal pattern evolution of vegetation NPP and its driving forces in middle-lower areas of the Min river based on geographical detector analyses[J]. Acta Ecologica Sinica, 2019,39(20):7621-7631.
[30]	张浚茂,臧传富.东南诸河流域1990~2015年土地利用时空变化特征及驱动机制[J]. 生态学报, 2019,39(24):9339-50. Zhang J M, Zang C F. Spatial and temporal variability characteristics and driving mechanisms of land use in the Southeastern River Basin from 1990 to 2015[J]. Acta Ecologica Sinica, 2019,39(24):9339-9350.
[31]	胡李发,谢元礼,崔思颖,等.关中平原城市群夏季城市热岛特征及驱动力[J]. 中国环境科学, 2021,41(8):3842-3852. Hu L F, Xie Y L, Cui S Y, et al. Characteristics and driving forces analysis of urban heatisland in Guanzhong plain urban agglomeration in summer[J]. China Environmental Science, 2021,41(8):3842-3852.
[32]	符静,秦建新,张猛,等.2001~2013年洞庭湖流域植被覆盖度时空变化特征[J]. 地球信息科学学报, 2016,18(9):1209-1216. Fu J, Qin J X, Zhang M, et al. Spatial-temporal variations of vegetation coverage in the Lake Dongting Basin from 2001 to 2013[J]. Journal of Geo-information Science, 2016,18(9):1209-1216.
[33]	Meng X, Gao X, Li S, et al. Spatial and temporal characteristics of vegetation NDVI changes and the driving forces in Mongolia during 1982~2015[J]. Remote Sensing, 2020,12(4):603.
[34]	Jiang C, Zhang H, Tang Z, et al. Evaluating the coupling effects of climate variability and vegetation restoration on ecosystems of the Loess Plateau, China[J]. Land Use Policy, 2017,69:134-148.
[35]	Guo B, Wang Y, Pei L, et al. Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014~2016[J]. Science of the Total Environmen, 2021,756:143869.
[36]	Yuan X, Shao Y, Wei X, et al. Study on the potential of cultivated land quality improvement based on a geological detector[J]. Geological Journal, 2018,53:387-397.
[37]	Zhu L, Meng J, Zhu L. Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin[J]. Ecological Indicators, 2020,117:106545.
[38]	Liu Y, Liu S, Sun Y, et al. Spatial differentiation of the NPP and NDVI and its influencing factors vary with grassland type on the Qinghai-Tibet Plateau[J]. Environmental Monitoring and Assessment, 2021, 193(1):48.
[39]	郭永强,王乃江,褚晓升,等.基于Google Earth Engine分析黄土高原植被覆盖变化及原因[J]. 中国环境科学, 2019,39(11):4804-4811. Analyzing vegetation coverage changes and its reasons on the Loess Plateau based on google earth engine.[J]. China Environmental Science, 2019,39(11):4804-4811.